Difference between revisions of "2018 NeuralNaturalLanguageInferenceM"

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Subject Headings: Natural Language Inference Task; Neural Natural Language Inference Task

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Abstract

Modeling natural language inference is a very challenging task. With the availability of large annotated data, it has recently become feasible to train complex models such as neural-network-based inference models, which have shown to achieve the state-of-the-art performance. Although there exist relatively large annotated data, can machines learn all knowledge needed to perform natural language inference (NLI) from these data? If not, how can neural-network-based NLI models benefit from external knowledge and how to build NLI models to leverage it? In this paper, we enrich the state-of-the-art neural natural language inference models with external knowledge. We demonstrate that the proposed models improve neural NLI models to achieve the state-of-the-art performance on the SNLI and MultiNLI datasets.

References

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 AuthorvolumeDate ValuetitletypejournaltitleUrldoinoteyear
2018 NeuralNaturalLanguageInferenceMQian Chen
Xiaodan Zhu
Zhen-Hua Ling
Diana Inkpen
Si Wei
Neural Natural Language Inference Models Enhanced with External Knowledge10.18653/v1/p18-12242018
AuthorQian Chen +, Xiaodan Zhu +, Zhen-Hua Ling +, Diana Inkpen + and Si Wei +
doi10.18653/v1/p18-1224 +
titleNeural Natural Language Inference Models Enhanced with External Knowledge +
year2018 +